Identification of Natural SARS-CoV-2 Infection in Seroprevalence Studies Among Vaccinated Populations

  1. Ryan T. Demmer
  2. Brett Baumgartner
  3. Talia D. Wiggen
  4. Angela K. Ulrich
  5. Ali J. Strickland
  6. Brianna M. Naumchik
  7. Bruno Bohn
  8. Sara Walsh
  9. Stephen Smith
  10. Susan Kline
  11. Steve D. Stovitz
  12. Stephanie Yendell
  13. Timothy J. Beebe
  14. Craig Hedberg

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Abstract

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  1. Version published to 10.1016/j.mayocp.2022.02.002
  2. ScreenIT

    SciScore for 10.1101/2021.04.12.21255330: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: The study was approved by the University of Minnesota Institutional Review Board.
    Consent: All participants provided informed consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Round 2 added a recombinant SARS-CoV-2 nucleocapsid spot, comprising amino acids 1-419, expressed in HEK293 cells.
    HEK293
    suggested: CLS Cat# 300192/p777_HEK293, RRID:CVCL_0045)
    Software and Algorithms
    SentencesResources
    Statistical Analysis: ‘Recent infection’ with SARS-CoV-2 was defined as a positive result from the round 1 (all round 1 assays were performed prior to vaccination) spike-only assay, or a self-report of recent infection; we consider this to be the definition of ‘true-infection’ (hereafter referred to as ‘infected’ or ‘infection’).
    Statistical Analysis
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.04.12.21255330v1 on medRxiv